Modeling and targeting tumor-immune signaling interactions in tumor microenvironment

肿瘤微环境中肿瘤免疫信号相互作用的建模和靶向

• 批准号: 10659993
• 负责人: Fuhai Li
• 金额: $ 35.06万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-16 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659993
• 关键词: 3-Dimensional; Artificial Intelligence; Atlases; Biological Assay; CRISPR library; Cells; Chemoresistance; Coculture Techniques; Communication; Communities; Complex; Data; Data Set; Development; Diagnosis; Disease; Drug Combinations; Feedback; Fibroblasts; Genetic; Genetic Transcription; Heterogeneity; Human; Immune; Immune signaling; Immunotherapy; In Vitro; Individual; Knock-out; Knowledge; Malignant Neoplasms; Measurable; Measures; Modeling; Molecular Target; Mus; Neoplasm Metastasis; Pancreatic Ductal Adenocarcinoma; Patients; Pharmaceutical Preparations; Play; Process; Proliferating; Research; Resources; Role; Signal Pathway; Signal Transduction; Systems Biology; Technology; Time; Training; Tumor Tissue; Tumor-Associated Process; Tumor-associated macrophages; Validation; angiogenesis; cancer genetics; cell type; effective therapy; improved; in vivo; migration; mouse model; neoplastic cell; new therapeutic target; novel; pancreatic ductal adenocarcinoma model; response; restraint; single-cell RNA sequencing; success; targeted treatment; tool; treatment response; tumor; tumor growth; tumor microenvironment; tumor progression

Project Summary Tumor-stroma/immune cell signaling communications within the tumor microenvironment (TME) play important roles in tumor development and responses to targeted and immunotherapies. However, our knowledge of complex signaling communications within TME, and their roles in tumor development, drug and immunotherapy response is limited. Effective molecular targets are still missing that can inhibit the tumor- stroma signaling communications. Single cell RNA sequencing (scRNA-seq) has been being a powerful technology to capture transcriptional changes in individual tumor, stroma, immune cells within TME. While scRNA-seq datasets of human cancer are rapidly growing in number, which is leading to many basic and translational discoveries, the study of dynamic tumor-stroma signaling communications is limited. Limiting factors include: 1) static and single time-point snapshots of the complex interactions within the TME, and 2) difficulty in perturbing a large number of related signaling targets; and measuring corresponding functional effects to these perturbations in mouse or tumor tissues (to identify novel therapeutic targets and treatments). To resolve these challenges, in this study, we propose to combine the cutting-edge technologies, including novel artificial intelligence (AI) models, scRNA-seq, crispr-based single or double knockouts (CDKOs), 3D tumor-CAF-TAM co-culture assays, and genetic mouse models, in a systems biology manner. Specifically, (in Aim 1), we will develop novel network AI models using valuable large sets of scRNA-seq data of PDAC human tumors at WashU to identify static core tumor-CAF-TAM interaction (TCTi) signaling networks (multi-cell intra- /inter-cellular signaling networks of TCTi); and an initial set of anti-TCTi targets. In Aim 2, we will further develop another network AI model (M-Step) to infer the better anti-TCTi targets using the functional validation feedbacks in Aim 3; and predict synergistic drug combinations (inhibiting multiple key anti-TCTi targets). In Aim 3, the predicted targets and drugs will be efficiently evaluated using scalable 3D Tumor-CAF-TAM co-culture assays and crispr-based knockouts (E-step) with 3 measurable metrics, i.e., tumor proliferation, migration, angiogenesis. The M-step (modeling) and E-step (validation) forms an E-M process to identify key anti- TCTi targets and drugs iteratively. We will apply these AI models in Pancreatic ductal adenocarcinoma (PDAC) because 1) there have been very limited responses to immunotherapy; 2) no effective treatment; 3) nearly all patients will develop chemo-resistant and metastatic tumors within 2 years of diagnosis. Also (feasibility), 4) we have a strong cross-disciplinary team studying the PDAC TME (supported by NCI SPORE and human tumor atlas network (HTAN)), with the valuable state-of-the-art resources. The success of this study will identify novel anti-tumor-TAM-CAF targets and drug cocktails for PDAC treatment. The AI models, supporting the novel E-M systems biology, can be applied to other cancers and diseases.

项目摘要 肿瘤细胞/免疫细胞信号传导肿瘤微环境（TME）的发挥很重要 在肿瘤发育中的作用以及对靶向和免疫疗法的反应。但是，我们对 TME内的复杂信号通信及其在肿瘤发育，药物和 免疫疗法反应有限。仍然缺少有效的分子靶标，可以抑制肿瘤 基质信号通信。单细胞RNA测序（SCRNA-SEQ）一直是强大的 捕获TME内单个肿瘤，基质，免疫细胞的转录变化的技术。尽管 人类癌症的SCRNA-seq数据集的数量迅速增长，这导致了许多基本和 翻译发现，动态肿瘤信号通信的研究有限。限制 因素包括：1）TME内复合物相互作用的静态和单个时间点快照，以及2） 在扰动大量相关信号目标方面的困难；并测量相应的功能 对小鼠或肿瘤组织中这些扰动的影响（以鉴定新的治疗靶标和治疗）。 为了解决这些挑战，在这项研究中，我们建议将尖端技术结合起来，包括 新颖的人工智能（AI）模型，SCRNA-SEQ，基于CRISPR的单敲门或双重淘汰赛（CDKOS），3D 肿瘤-CAF-TAM共培养分析和遗传小鼠模型以系统生物学方式。具体，（in AIM 1），我们将使用有价值的PDAC人类的大量SCRNA-SEQ数据开发新型网络AI模型 WASHU的肿瘤以鉴定静态核心肿瘤-CAF-TAM相互作用（TCTI）信号网络（多细胞内部 - /TCTI的细胞间信号网络）；以及一组初始的抗TCTI靶标。在AIM 2中，我们将进一步 开发另一个网络AI模型（M-Step），以使用功能验证来推断更好的抗TCTI目标 AIM 3中的反馈；并预测协同的药物组合（抑制多个关键的抗TCTI靶标）。目标 3，将使用可伸缩的3D肿瘤-CAF-TAM共培养来有效评估预测的靶标和药物 测定和基于CRISPR的淘汰（E-Step），具有3个可测量指标，即肿瘤增殖，迁移， 血管生成。 M-step（建模）和E-Step（验证）形成了E-M过程，以识别关键反抗 TCTI靶向和药物迭代。我们将在胰腺导管腺癌中应用这些AI模型 （PDAC）因为1）对免疫疗法的反应非常有限； 2）没有有效的治疗； 3） 几乎所有患者都会在诊断后的2年内发展出耐化学和转移性肿瘤。还 （可行性），4）我们有一个强大的跨学科团队研究PDAC TME（由NCI Spore支持 和人类肿瘤网络（HTAN），具有宝贵的最新资源。这个成功 研究将确定用于PDAC治疗的新型抗肿瘤-TAM-CAF靶标和药物鸡尾酒。 AI模型， 支持新型的E-M系统生物学，可以应用于其他癌症和疾病。